Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis to reveal that ignoring the motion of moving objects can result in serious localization bias. Therefore, we propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. In contrast to previous global Bird-Eye-View (BEV) methods, DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. By iteratively refining the estimated object motion and location, the preceding features can be precisely aggregated to the current frame to mitigate the aforementioned adverse effects. The simple framework has two significant appealing properties. It is flexible and practical that can be plugged into most camera-based 3D object detectors. As there are predictions of object motion in the loop, it can easily track objects across frames according to their nearest center distances. Without bells and whistles, DORT outperforms all the previous methods on the nuScenes detection and tracking benchmarks with 62.5\% NDS and 57.6\% AMOTA, respectively. The source code will be released.

翻译：近期多摄像头3D目标检测器通常利用时序信息构建多视角立体匹配，以缓解不适定的深度估计问题。然而，这类方法通常假设所有物体均为静态，并在不同帧间直接聚合特征。本文通过理论与实证分析揭示，忽视运动物体的运动状态会导致严重的定位偏差。为此，我们提出动态对象循环建模（DORT）方法应对该问题。与先前全局鸟瞰图（BEV）方法不同，DORT提取物体级局部体积进行运动估计，同时减轻了计算负担。通过迭代优化估计的物体运动与位置，前帧特征可精确聚合至当前帧，从而缓解上述不利影响。该简洁框架具有两个显著优势：灵活实用，可嵌入多数基于摄像头的3D目标检测器；由于循环中预测物体运动，可根据最近中心距离轻松实现跨帧目标跟踪。无需额外技巧，DORT在nuScenes检测与跟踪基准上分别以62.5% NDS和57.6% AMOTA超越所有先前方法。源代码将公开。